Utility of Ki67 immunostaining in the grading of pineal parenchymal tumours: a multicentre study
Bibliographic record
Abstract
M. Fèvre‐Montange, A. Vasiljevic, D. Frappaz, J. Champier, A. Szathmari, M.‐H. Aubriot Lorton, F. Chapon, A. Coulon, I. Quintin Roué, M.‐B. Delisle, D. Figarella‐Branger, A. Laquerrière, C. Miquel, J.‐F. Michiels, M. Péoch, M. Polivka, F. Fauchon and A. Jouvet (2012) Neuropathology and Applied Neurobiology38, 87–94 Utility of Ki67 immunostaining in the grading of pineal parenchymal tumours: a multicentre study Aims: Pineal parenchymal tumours (PPTs) are rare neoplasms that are divided into pineocytoma (PC), pineoblastoma (PB) and PPT of intermediate differentiation (PPTID). Factors affecting the survival of patients with PPTs are morphological subtype and histological grading according to mitotic index and neurofilament immunostaining. Grading criteria to distinguish PPTIDs are difficult to define, particularly when using small specimens. The Ki67 labelling index (LI) might be helpful in distinguishing between grade II and III PPTIDs. Our study was performed to assess the predictive value of the Ki67 LI in a large cooperative series of PPTs and to evaluate whether inclusion of this data would improve and refine the World Health Organization classification. Methods: A retrospective analysis of 33 PPTs was performed. The histological features of the tumours were reviewed and Ki67 LI scoring was evaluated by immunohistochemistry. Data were correlated with the patients' survival. Results: The mean Ki67 LI was significantly different for tumour grades (0 in PC, 5.2 ± 0.4 in PPTID grade II, 11.2 ± 2.0 in PPTID grade III, 36.4 ± 6.2 in PB; P < 0.0001). However, there was no statistically significant difference in either overall or disease‐free survival evaluated by the Kaplan–Meier method for patients with different grade tumours or Ki67 LI, possibly due to the different clinical management of patients in different centres. Conclusions: The Ki67 LI may be a useful additional tool for grading PPTs, more particularly in small tumour samples.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".